private void forward_convolute_on_depth(UnitLayer to, int stride, int depth, int length)
{
int to_depth = depth + length;
if (to_depth >= to.Depth)
{
to_depth = to.Depth;
}
for (int d = depth; d < to_depth; d++)
{
WeightLayer weights = WeightLayerPool.find(Id, to.Id, d);
int width_of_weights = weights.Width, height_of_weights = weights.Height;
int width_of_units = to.Width, height_of_units = to.Height;
for (int y = 0; y < height_of_units; y++)
{
int top = y * stride;
int bottom = top + height_of_weights;
for (int x = 0; x < width_of_units; x++)
{
int left = x * stride;
to.set_value_at_inport(x, y, d, weights.convolute_product(left, top, left + width_of_weights, bottom, Units));
}
}
}
}
public void forward_average_pool(UnitLayer to, int width, int height, int stride)
{
int width_of_units = to.Width, height_of_units = to.Height;
for (int d = 0; d < Depth; d++)
{
for (int y = 0; y < height_of_units; y++)
{
int top = y * stride;
int bottom = top + height;
for (int x = 0; x < width_of_units; x++)
{
int left = x * stride;
to.set_value_at_inport(x, y, d, average_pool(left, top, left + width, bottom, d));
}
}
}
}
private void forward_fully_connect_on_depth(UnitLayer to, int depth, int length)
{
int width = to.Width, height = to.Height;
int to_depth = depth + length;
if (to_depth >= to.Depth)
{
to_depth = to.Depth;
}
for (int d = depth; d < to_depth; d++)
{
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
WeightLayer weights = WeightLayerPool.find(Id, to.Id, x, y, d);
to.set_value_at_inport(x, y, d, weights.fully_product(Units));
}
}
}
}
